2 mai 2020
This course provides an introduction of some important concepts and tools on a very important aspect of data science: cleaning and organizing data before any analysis. A must for any data scientist.
25 oct. 2016
This course is really a challenging and compulsory for any one who wants to be a data scientist or working in any sort of data. It teaches you how to make very palatable data-set fro ma messy data.
par Aki T•
24 oct. 2019
This course was excellent and fundamental in order to even start a data analysis. It sets the foundation for how to read and treat the data, which is as the instructor mentioned, often overlooked. Thank you very much for taking the time to break the cleaning process into each comprehensive pieces.
par Nino P•
24 mai 2019
A bit tough course with topics of getting the data since I don't know much about file types, but cleaning part is a must do for every data scientist. dplyr and tidyverse is the base of R and nowadays I only use dplyr for my data wrangling. Highly recommendable course and specialization.
par Sudheergouda P•
31 déc. 2018
The course project was really helpfull in understanding how the data is presented to datascientists. Now to get the jist of the data we have to go through assembling, cleaning and cutting the data.. It was a challenged to understand the data.. assembling the data was a lot of fun in R..
par Fernando V•
14 déc. 2016
A great course. I mean, It has not been easy, I have spent a lot of time in front of the PC practising and doing exercises, but this time and the tools that I have learned make me much more agile and confortable with R, and I have seen the big possibilities that this language has.
par Christopher L•
17 juil. 2017
great course, I am fairly familiar with R in my line of work but this was a great opportunity to practice web-scraping. I might even switch from a dplyr-centric wrangling workflow to one centered on data.table in my personal and professional work. more compact and faster!
par Carlos M•
21 déc. 2016
Difficult but valuable. You will be watching the videos repeatedly and become a regular at StockOverflow but it was completely worth it. Getting, cleaning, and processing data is pretty much 80%+ of the job, this course's information is vital to any future data worker.
par Gilvan S•
11 févr. 2017
Excellent course. It gets through the "dirty job" of obtaining data from diverse sources (including API, web, and others), cleaning it, and transforming it into a "tidy" dataset. Highly recommended, along with the R programming course (which you should take first).
par Scott C•
17 févr. 2018
Good overview of what it means to get and clean your own data. Really enjoyed the final project as it challenged you to, with minimal guidance, think through what a tidy dataset really means, and figure out how to make that happen with the dataset you are provided.
par Tim S•
23 mars 2016
For someone with no programming background and limited experience working with data, this was a challenging, sometimes frustrating, course. But perseverance through the struggle can end in a deep sense of satisfaction. Happily, this is how it was - quite rewarding.
7 sept. 2016
Wonderful course. gets you through the basics and beyond in getting and cleaning data from diverse sources. Very well thought and explained. There is a lot to be learnt from this course, and it requires devoting a good amount of time to let the material sink in.
par Diego A S R•
4 juil. 2020
Good course, but needs an update. Week 2 was really difficult compared to what was explained in the lectures and regex expressions should be explained using R, it was a little hard to learn to use them directly in R. I feel that I learned a lot in this course.
par Renzzo S S•
16 nov. 2020
Excellent course! i learned a lot with the packages mentioned dplyr, tidyr, readr, lubridate. the swirl package is perfect to learn by doing and the assignment is very challenging and it is good because it incentivates you to research deeply and learn more.
par Randal N•
23 janv. 2018
Very enlightening course. It is the first course where I felt like I was actually doing something data sciency. Would recommend even as a stand alone course because I have now come to appreciate the importance of tidy data in performing successful analyses.
par Keat C C•
7 nov. 2016
Really can learn practical skills! I like that each sub course of data science specialisation just focus on a certain areas and takes only 4 weeks, this way I won't be overburden between work and learning, and also easier for me to absorb the new skills.
par Waleed A•
31 janv. 2018
Another brilliant course from Johns Hopkins University in the data science specialisation. Data preparation is a step where an analyst may spend considerable time before beginning any analysis task. I found this course useful and practical. It provided
par Daniel M D V•
3 sept. 2019
Excellent! From my point of view, this is the best course so far. The general concepts that are thought here can be applied to any programming language you use for data analysis. The specific R concepts really shows the power R has to manipulate data.
par Kunal P•
15 déc. 2019
This was one of the best class. Recommend more side reading material on data. SWIRL has a reading link but the link is not provided anywhere else on the board. Also, it would be beneficial if the links can be made clickable in lecture slides. Thanks.
par Martin H•
14 août 2016
Exellent course, which brings you to the next level of a Data Scientist.
Getting and Cleaning data principles can be used in alot of situations. I found the build up of this and the assignment at the end to be very well tought trough and important.
par Oleksandr K•
14 avr. 2018
Very good course and lectures. However, it would be good to have a book covering all of the material in this course. That would make work on final project much easier. In my opinion, it is impossible to finish final project in just 2 hours.
par Kristin K•
4 août 2017
This course solidified any gaps that were left from the R Programming Course and opens the world of data science to everyone in a very practical way. I really enjoyed the presentation of the material and am very happy I took the class.
8 juin 2017
This was so hard to me, because I didn't know anything about 'Making tidy dataset'. So, when I took a course project, I was struggling to find 'what should I do'. Comprehending raw data is so hard then you think, newbies! Be careful!
par Jan K•
7 mars 2017
Covers a wide range of topics without loosing transparency. In my opinion requires more work than the other courses, but is really worth a go. You end up having a firm basis for working with data and learning more about the process.
par Tomer E•
21 juin 2020
Very nice course.
helped to understand how to find sources of data (I found that extremely important), and strengthened my R skills.
It would be nice though to have the links which were shown in the slides available for the students.
par Miguel C•
20 déc. 2017
This is a very complete course. It covers the basics of what you have to know to adquire data from different sources and filter that data to be used in further steps of data analysis. It offered great notions on Data Mining also.
par Tim S•
17 sept. 2017
I learned a lot. The videos were clear and helpful. The assignments were just the right level, not too easy and not hard but still challenging.
The swirl package for interactive practice/learning is also very helpful. I Love it!